Yearly Traffic Safety Analysis

497 CRASHES IN
IOWA, IA
2025

All metrics benchmarked against2024

In 2025, Wapello County recorded 497 total crashes, a 10.6% decrease from the 556 crashes reported in 2024. Despite the overall reduction in collisions, the total number of injuries rose from 186 to 221. Fatalities also decreased, with 2 deaths in 2025 compared to 4 in the prior year.

497

▼ -10.6%was 556

Total Crash Events

2

▼ -50.0%was 4

Persons Killed

221

▲ 18.8%was 186

Persons Injured

2

▼ -50.0%was 4

Fatal Crash Events

Note: "Persons Killed" (2) counts individual fatalities across all crash events. "Fatal" in the severity table below (2) counts crash events where at least one fatality occurred. A single crash can result in multiple fatalities.

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Aggregate counts from crash, person, and vehicle records

Trend Summary

Overall traffic crashes in Wapello County showed a downward trend, decreasing by 10.6% from 556 in 2024 to 497 in 2025. While total crashes and fatalities (down from 4 to 2) declined, the number of people injured in these incidents increased by 18.8%, rising from 186 to 221.

Vulnerable Road User Casualties

0

Pedestrians Killed

Prior: 0→0.0%

0

Cyclists Killed

Prior: 1↓-100.0%

2

Motorists Killed

Prior: 3↓-33.3%

9

Pedestrians Injured

Prior: 4↑125.0%

5

Cyclists Injured

Prior: 1↑400.0%

207

Motorists Injured

Prior: 179↑15.6%

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Mode classified from person records (driver/passenger → motorist; pedestrian; bicyclist → cyclist; in-line skater / unspecified → other)

When Crashes Happen

The timing of crashes shifted between the two periods. In 2025, the peak day for crashes was Wednesday with 94 incidents, a change from Friday (113 incidents) in 2024. The peak hour also moved from the evening commute at 5 p.m. (46 crashes) in the prior year to the morning at 7 a.m. (45 crashes) in the current year.

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash date field aggregated by weekday

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Crash time field aggregated by hour (0-23)

Crash Severity Breakdown

Crash severity worsened in 2025 despite fewer total incidents. The proportion of crashes resulting in any level of injury increased from 27.5% in 2024 to 34.8% in 2025. Specifically, serious injury crashes rose from 11 to 16, and their share of all crashes increased from 2.0% to 3.2%. Conversely, fatal crashes decreased from 4 to 2, and the share of no-injury crashes fell from 71.8% to 64.8%.

Outcome by Severity (Crash Events)

Fatal2fatal crashes0.4%
↓-50.0%prior 4
Serious Injury16serious injury crashes3.2%
↑45.5%prior 11
Minor Injury63minor injury crashes12.7%
↑28.6%prior 49
Possible Injury94possible injury crashes18.9%
↑1.1%prior 93
No Injury322no injury crashes64.8%
↓-19.3%prior 399

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · KABCO injury classification scale

Severity Distribution (Crash Events)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Most severe injury per crash record

Top Contributing Factors

Collisions involving an 'Animal' remained the leading contributing factor in both years, though the count decreased by 30.1% from 103 in 2024 to 72 in 2025. Crashes due to 'Failure to yield right of way from a stop sign' increased by 27.5% in count (from 40 to 51 incidents), becoming a more prominent second-leading factor. Notably, crashes attributed to 'Ran off road - left' saw a 51.7% increase in count, rising from 29 to 44 and moving into the top three factors in 2025.

Officer-Reported Primary Contributing Cause

Animal72 (14.5%)↓-30.1%prior 103
FTYROW: From stop sign51 (10.3%)↑27.5%prior 40
Ran off road - left44 (8.9%)↑51.7%prior 29
Ran Stop Sign34 (6.8%)↑6.3%prior 32
FTYROW: Making left turn25 (5%)↓-16.7%prior 30
Driving too fast for conditions25 (5%)↑47.1%prior 17
Lost Control23 (4.6%)↓-36.1%prior 36
Followed too close22 (4.4%)↓-38.9%prior 36
Ran off road - straight19 (3.8%)↑35.7%prior 14
Driver Distraction: Other interior distraction15 (3%)↑36.4%prior 11

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Officer-reported primary contributory cause per crash

Road & Environmental Conditions

The distribution of crashes across lighting and weather conditions remained relatively stable year-over-year, with most incidents in both periods occurring in daylight (59.8% in 2025 vs. 58.5% in 2024) and clear weather (68.4% vs. 67.3%). However, there was a notable shift in road surface conditions. The proportion of crashes occurring on non-dry surfaces—such as wet, snow, or ice—increased from 14.7% in 2024 to 20.7% in 2025.

Weather

Clear340 (79.6%)
↓-9.1%prior 374
Cloudy31 (7.3%)
↓-39.2%prior 51
Snow25 (5.9%)
↑78.6%prior 14
Rain19 (4.4%)
↑11.8%prior 17
Freezing rain/drizzle6 (1.4%)
Fog, smoke, smog3 (0.7%)
↓-57.1%prior 7
Blowing Snow3 (0.7%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Weather condition at time of crash

Lighting

Daylight297 (69.2%)
↓-8.6%prior 325
Dark - roadway lighted67 (15.6%)
↑4.7%prior 64
Dark - roadway not lighted44 (10.3%)
↓-20.0%prior 55
Dusk14 (3.3%)
↑180.0%prior 5
Dawn4 (0.9%)
↓-73.3%prior 15
Dark - unknown roadway lighting3 (0.7%)
↓-57.1%prior 7

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Lighting condition field

Road Surface

Dry323 (75.5%)
↓-16.5%prior 387
Wet38 (8.9%)
↑11.8%prior 34
Snow34 (7.9%)
↑161.5%prior 13
Ice/frost21 (4.9%)
↓-4.5%prior 22
Slush5 (1.2%)
→0.0%prior 5
Gravel2 (0.5%)
↓-66.7%prior 6
Sand2 (0.5%)
Mud, dirt2 (0.5%)
Other (explain in narrative)1 (0.2%)

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Road surface condition field

Vehicles & Demographics

The top vehicle makes involved in crashes remained consistent, with Ford (132 vehicles) and Chevrolet (123 vehicles) leading in 2025, just as they did in 2024 (148 and 132 vehicles, respectively). An analysis of persons involved shows a shift in age demographics. The proportion of individuals aged 16-20 involved in crashes increased from 11.5% in 2024 to 15.4% in 2025, while the share for the 26-34 age group decreased from 16.7% to 12.8%.

Top Vehicle Makes (805 vehicles)

1
FORD132 (16.4%)
↓-10.8%prior 148
2
CHEV123 (15.3%)
↓-6.8%prior 132
3
TOYT59 (7.3%)
↑1.7%prior 58
4
GMC48 (6%)
↑23.1%prior 39
5
DODG46 (5.7%)
↓-14.8%prior 54
6
JEEP41 (5.1%)
↓-12.8%prior 47
7
CHEVROLET33 (4.1%)
↓-29.8%prior 47
8
BUIC26 (3.2%)
↑13.0%prior 23
9
NISS23 (2.9%)
→0.0%prior 23
10
KIA23 (2.9%)
↑27.8%prior 18

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Vehicle unit records

80 persons with unknown or unrecorded age excluded from age chart.

Sex Distribution (493 persons with recorded sex)

Male288 (58.4%)
↓-17.7%prior 350
Female205 (41.6%)
↓-13.5%prior 237

Source: Iowa Crash Data · ArcGIS Open Data · 2025-01-01 to 2025-12-31 · Person-level records linked to crash events

Data Sources & Methodology

Primary Data Source

All crash data in this report is sourced from Iowa Crash Data, accessed programmatically via the ArcGIS Open Data API (SODA). This dataset contains official police-reported motor vehicle traffic crash records maintained by the reporting jurisdiction's law enforcement agency. Records are published to the open data portal by the municipality and are subject to the portal's terms of use.

Data Retrieval

  • Access method: ArcGIS Open Data API (SoQL queries)
  • Data format: Structured JSON via REST API
  • Record types queried: Crash events, person records, and vehicle unit records
  • Date filter applied: 2025-01-01 through 2025-12-31
  • Report generated: September 9, 2026

Data Coverage

  • Reporting period: 2025-01-01 through 2025-12-31 (365 days)
  • Geographic scope: iowa, IA
  • Total crash records analyzed: 497
  • Total persons involved: 868
  • Total vehicles involved: 805

Analytical Methodology

  • Severity classification: Uses the KABCO injury scale (K=Fatal, A=Incapacitating injury, B=Non-incapacitating injury, C=Possible injury, O=No injury/property damage only), the standard classification in U.S. Model Minimum Uniform Crash Criteria (MMUCC). Severity is assigned per crash event based on the most severe injury in that crash. A single fatal crash (K) may involve multiple fatalities; therefore the "Persons Killed" count in the headline KPIs may differ from the "Fatal" crash count in the severity breakdown.
  • Contributing factors: Reflect the officer-determined primary contributory cause recorded at the time of the crash report. These are preliminary determinations and may not reflect final investigation findings.
  • Hit-and-run classification: Based on the hit-and-run indicator field in the official crash report, as determined by the responding officer at the scene.
  • Temporal analysis: Day-of-week and hour-of-day distributions are computed from the crash date/time timestamp in each record.
  • Demographics: Age and sex distributions are drawn from person-level records linked to each crash event. A single crash may involve multiple persons.
  • Vehicle data: Make information is drawn from vehicle unit records linked to each crash event.
  • AI commentary: Narrative sections are generated by Google Gemini (large language model) based on the structured data. Commentary is descriptive, not predictive, and should not be interpreted as expert opinion.

Limitations & Disclaimers

  • Only crashes reported to and documented by law enforcement are included. Minor incidents, unreported crashes, and near-misses are not captured in this dataset.
  • Data reflects conditions at the time of the initial police report and may be subject to subsequent corrections, reclassifications, or supplements by the reporting agency.
  • Open data portal records may experience a publication lag - recently occurring crashes may not yet appear in the dataset at the time of report generation.
  • AI-generated commentary is produced by a large language model and is intended to highlight patterns in the data. It does not constitute legal, medical, or professional analysis.
  • Percentages are calculated from reported data and are subject to rounding.

Non-Affiliation Disclosure

This report is produced independently by ThatCarHitMe.com (Injuria.ai). It is not affiliated with, endorsed by, or produced in partnership with any law enforcement agency, municipal government, state department of transportation, or the National Highway Traffic Safety Administration (NHTSA). Data is sourced from publicly available government open data portals.

Data License

The underlying crash data is provided under the municipality's Open Data Terms of Use and is made available to the public for unrestricted use. This analysis and report is © 2026 Injuria.ai and may be cited with attribution using the suggested citation below.

Corrections & Feedback

If you believe any data in this report is inaccurate or have questions about our methodology, please contact: data@injuria.ai. We are committed to accuracy and will issue corrections promptly.

Suggested Citation

ThatCarHitMe.com (Injuria.ai). "iowa, IA Crash Intelligence Report: 2025." Published September 9, 2026. Reporting period: 2025-01-01 to 2025-12-31. Data source: Iowa Crash Data, ArcGIS Open Data. Available at: https://thatcarhitme.com/crash-data/iowa/statewide/2025-annual-report

About the Publisher

ThatCarHitMe.com is a crash data intelligence platform developed by Injuria.ai, a legal technology company specializing in traffic safety analytics. We aggregate and analyze publicly available government crash data to produce structured intelligence reports for communities, researchers, journalists, and legal professionals. Our reports combine programmatic data retrieval from official open data portals with AI-assisted narrative analysis.

Questions about this report's data or methodology: data@injuria.ai

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